Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations3491
Missing cells929
Missing cells (%)1.6%
Duplicate rows31
Duplicate rows (%)0.9%
Total size in memory416.0 KiB
Average record size in memory122.0 B

Variable types

Numeric8
Text3
Unsupported1
Categorical2
DateTime1
Boolean2

Alerts

cooperate has constant value "True"Constant
Dataset has 31 (0.9%) duplicate rowsDuplicates
baths is highly overall correlated with beds and 5 other fieldsHigh correlation
beds is highly overall correlated with baths and 5 other fieldsHigh correlation
max_price is highly overall correlated with baths and 5 other fieldsHigh correlation
net_price is highly overall correlated with baths and 5 other fieldsHigh correlation
new_price is highly overall correlated with baths and 5 other fieldsHigh correlation
old_price is highly overall correlated with baths and 5 other fieldsHigh correlation
sqft is highly overall correlated with baths and 5 other fieldsHigh correlation
concession_title has 905 (25.9%) missing valuesMissing
unit_number is an unsupported type, check if it needs cleaning or further analysisUnsupported
baths has 37 (1.1%) zerosZeros

Reproduction

Analysis started2024-09-19 13:11:04.755199
Analysis finished2024-09-19 13:11:21.611609
Duration16.86 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

building_id
Real number (ℝ)

Distinct130
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1375.0163
Minimum1
Maximum6378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:21.813812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q156
median103
Q3293
95-th percentile6374
Maximum6378
Range6377
Interquartile range (IQR)237

Descriptive statistics

Standard deviation2431.6147
Coefficient of variation (CV)1.768426
Kurtosis0.18788249
Mean1375.0163
Median Absolute Deviation (MAD)79
Skewness1.4442497
Sum4800182
Variance5912749.9
MonotonicityNot monotonic
2024-09-19T06:11:22.063797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6274 230
 
6.6%
6374 186
 
5.3%
6279 154
 
4.4%
293 124
 
3.6%
104 121
 
3.5%
95 115
 
3.3%
100 99
 
2.8%
97 92
 
2.6%
91 74
 
2.1%
21 64
 
1.8%
Other values (120) 2232
63.9%
ValueCountFrequency (%)
1 58
1.7%
2 24
0.7%
3 36
1.0%
4 29
0.8%
6 34
1.0%
9 4
 
0.1%
10 54
1.5%
11 1
 
< 0.1%
13 16
 
0.5%
15 31
0.9%
ValueCountFrequency (%)
6378 26
 
0.7%
6377 10
 
0.3%
6374 186
5.3%
6360 3
 
0.1%
6290 7
 
0.2%
6289 1
 
< 0.1%
6280 1
 
< 0.1%
6279 154
4.4%
6274 230
6.6%
6270 18
 
0.5%

name
Text

Distinct129
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:22.776499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length27
Mean length15.581495
Min length3

Characters and Unicode

Total characters54395
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st row1000 South Clark
2nd row1000 South Clark
3rd row1000 South Clark
4th row1000 South Clark
5th row1000 South Clark
ValueCountFrequency (%)
the 411
 
4.5%
apartments 409
 
4.5%
park 353
 
3.9%
alta 312
 
3.5%
grand 256
 
2.8%
north 244
 
2.7%
central 230
 
2.5%
at 224
 
2.5%
residences 200
 
2.2%
aspire 186
 
2.1%
Other values (188) 6214
68.7%
2024-09-19T06:11:23.680276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5548
 
10.2%
e 5025
 
9.2%
r 4382
 
8.1%
a 4035
 
7.4%
t 3585
 
6.6%
n 2911
 
5.4%
o 2546
 
4.7%
s 2221
 
4.1%
i 2149
 
4.0%
l 1994
 
3.7%
Other values (52) 19999
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37704
69.3%
Uppercase Letter 9102
 
16.7%
Space Separator 5548
 
10.2%
Decimal Number 2009
 
3.7%
Math Symbol 18
 
< 0.1%
Other Punctuation 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5025
13.3%
r 4382
11.6%
a 4035
10.7%
t 3585
9.5%
n 2911
 
7.7%
o 2546
 
6.8%
s 2221
 
5.9%
i 2149
 
5.7%
l 1994
 
5.3%
h 1470
 
3.9%
Other values (15) 7386
19.6%
Uppercase Letter
ValueCountFrequency (%)
A 1311
14.4%
P 889
9.8%
S 884
9.7%
T 859
9.4%
C 773
 
8.5%
M 605
 
6.6%
L 550
 
6.0%
N 460
 
5.1%
R 402
 
4.4%
O 386
 
4.2%
Other values (14) 1983
21.8%
Decimal Number
ValueCountFrequency (%)
0 521
25.9%
1 382
19.0%
5 342
17.0%
3 329
16.4%
2 180
 
9.0%
7 69
 
3.4%
9 65
 
3.2%
4 50
 
2.5%
6 49
 
2.4%
8 22
 
1.1%
Space Separator
ValueCountFrequency (%)
5548
100.0%
Math Symbol
ValueCountFrequency (%)
+ 18
100.0%
Other Punctuation
ValueCountFrequency (%)
& 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46806
86.0%
Common 7589
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5025
 
10.7%
r 4382
 
9.4%
a 4035
 
8.6%
t 3585
 
7.7%
n 2911
 
6.2%
o 2546
 
5.4%
s 2221
 
4.7%
i 2149
 
4.6%
l 1994
 
4.3%
h 1470
 
3.1%
Other values (39) 16488
35.2%
Common
ValueCountFrequency (%)
5548
73.1%
0 521
 
6.9%
1 382
 
5.0%
5 342
 
4.5%
3 329
 
4.3%
2 180
 
2.4%
7 69
 
0.9%
9 65
 
0.9%
4 50
 
0.7%
6 49
 
0.6%
Other values (3) 54
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5548
 
10.2%
e 5025
 
9.2%
r 4382
 
8.1%
a 4035
 
7.4%
t 3585
 
6.6%
n 2911
 
5.4%
o 2546
 
4.7%
s 2221
 
4.1%
i 2149
 
4.0%
l 1994
 
3.7%
Other values (52) 19999
36.8%

unit_number
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size27.4 KiB

beds
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
1
1744 
0
909 
2
718 
3
 
118
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3491
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1744
50.0%
0 909
26.0%
2 718
20.6%
3 118
 
3.4%
4 2
 
0.1%

Length

2024-09-19T06:11:23.930325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T06:11:24.223093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1744
50.0%
0 909
26.0%
2 718
20.6%
3 118
 
3.4%
4 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1744
50.0%
0 909
26.0%
2 718
20.6%
3 118
 
3.4%
4 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3491
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1744
50.0%
0 909
26.0%
2 718
20.6%
3 118
 
3.4%
4 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1744
50.0%
0 909
26.0%
2 718
20.6%
3 118
 
3.4%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1744
50.0%
0 909
26.0%
2 718
20.6%
3 118
 
3.4%
4 2
 
0.1%

baths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2348897
Minimum0
Maximum3
Zeros37
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:24.426615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48008787
Coefficient of variation (CV)0.38876984
Kurtosis1.6333152
Mean1.2348897
Median Absolute Deviation (MAD)0
Skewness1.3548895
Sum4311
Variance0.23048436
MonotonicityNot monotonic
2024-09-19T06:11:24.612433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2636
75.5%
2 703
 
20.1%
3 47
 
1.3%
1.5 42
 
1.2%
0 37
 
1.1%
2.5 26
 
0.7%
ValueCountFrequency (%)
0 37
 
1.1%
1 2636
75.5%
1.5 42
 
1.2%
2 703
 
20.1%
2.5 26
 
0.7%
3 47
 
1.3%
ValueCountFrequency (%)
3 47
 
1.3%
2.5 26
 
0.7%
2 703
 
20.1%
1.5 42
 
1.2%
1 2636
75.5%
0 37
 
1.1%

sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct679
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean800.94758
Minimum229
Maximum3249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:24.830431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum229
5-th percentile485
Q1608
median731
Q3896
95-th percentile1314.5
Maximum3249
Range3020
Interquartile range (IQR)288

Descriptive statistics

Standard deviation290.34789
Coefficient of variation (CV)0.36250549
Kurtosis6.0348062
Mean800.94758
Median Absolute Deviation (MAD)138
Skewness1.8443109
Sum2796108
Variance84301.898
MonotonicityNot monotonic
2024-09-19T06:11:25.080466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 67
 
1.9%
750 44
 
1.3%
656 39
 
1.1%
716 33
 
0.9%
740 29
 
0.8%
565 28
 
0.8%
785 26
 
0.7%
570 26
 
0.7%
759 25
 
0.7%
715 24
 
0.7%
Other values (669) 3150
90.2%
ValueCountFrequency (%)
229 1
 
< 0.1%
247 2
0.1%
287 2
0.1%
288 4
0.1%
310 1
 
< 0.1%
316 1
 
< 0.1%
320 2
0.1%
324 1
 
< 0.1%
336 3
0.1%
350 1
 
< 0.1%
ValueCountFrequency (%)
3249 1
< 0.1%
3050 1
< 0.1%
2700 1
< 0.1%
2466 1
< 0.1%
2298 1
< 0.1%
2280 1
< 0.1%
2267 2
0.1%
2256 1
< 0.1%
2239 1
< 0.1%
2179 1
< 0.1%
Distinct28
Distinct (%)0.8%
Missing24
Missing (%)0.7%
Memory size27.4 KiB
South Loop
1194 
River North
490 
West Loop
306 
Streeterville
259 
The Loop
204 
Other values (23)
1014 

Length

Max length21
Median length15
Mean length10.438419
Min length6

Characters and Unicode

Total characters36190
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowSouth Loop
2nd rowSouth Loop
3rd rowSouth Loop
4th rowSouth Loop
5th rowSouth Loop

Common Values

ValueCountFrequency (%)
South Loop 1194
34.2%
River North 490
14.0%
West Loop 306
 
8.8%
Streeterville 259
 
7.4%
The Loop 204
 
5.8%
Old Town 186
 
5.3%
Gold Coast 185
 
5.3%
New East Side 121
 
3.5%
Old town 83
 
2.4%
Lincoln Park 72
 
2.1%
Other values (18) 367
 
10.5%

Length

2024-09-19T06:11:25.342714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loop 1704
25.0%
south 1195
17.5%
river 591
 
8.7%
north 516
 
7.6%
west 343
 
5.0%
old 269
 
3.9%
town 269
 
3.9%
streeterville 259
 
3.8%
the 204
 
3.0%
coast 185
 
2.7%
Other values (26) 1277
18.7%

Most occurring characters

ValueCountFrequency (%)
o 6090
16.8%
3345
 
9.2%
t 3236
 
8.9%
e 2791
 
7.7%
r 1934
 
5.3%
h 1916
 
5.3%
L 1822
 
5.0%
p 1715
 
4.7%
S 1607
 
4.4%
u 1332
 
3.7%
Other values (31) 10402
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26116
72.2%
Uppercase Letter 6729
 
18.6%
Space Separator 3345
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6090
23.3%
t 3236
12.4%
e 2791
10.7%
r 1934
 
7.4%
h 1916
 
7.3%
p 1715
 
6.6%
u 1332
 
5.1%
i 1295
 
5.0%
l 1273
 
4.9%
v 918
 
3.5%
Other values (11) 3616
13.8%
Uppercase Letter
ValueCountFrequency (%)
L 1822
27.1%
S 1607
23.9%
N 664
 
9.9%
R 610
 
9.1%
T 390
 
5.8%
W 347
 
5.2%
O 269
 
4.0%
G 215
 
3.2%
C 186
 
2.8%
E 127
 
1.9%
Other values (9) 492
 
7.3%
Space Separator
ValueCountFrequency (%)
3345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32845
90.8%
Common 3345
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6090
18.5%
t 3236
 
9.9%
e 2791
 
8.5%
r 1934
 
5.9%
h 1916
 
5.8%
L 1822
 
5.5%
p 1715
 
5.2%
S 1607
 
4.9%
u 1332
 
4.1%
i 1295
 
3.9%
Other values (30) 9107
27.7%
Common
ValueCountFrequency (%)
3345
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 6090
16.8%
3345
 
9.2%
t 3236
 
8.9%
e 2791
 
7.7%
r 1934
 
5.3%
h 1916
 
5.3%
L 1822
 
5.0%
p 1715
 
4.7%
S 1607
 
4.4%
u 1332
 
3.7%
Other values (31) 10402
28.7%

concession_title
Text

MISSING 

Distinct67
Distinct (%)2.6%
Missing905
Missing (%)25.9%
Memory size27.4 KiB
2024-09-19T06:11:25.826922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length203
Median length157
Mean length65.682135
Min length1

Characters and Unicode

Total characters169854
Distinct characters70
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row1 Month Off Available on all unit types for a limited time!
2nd row1 Month Off Available on all unit types for a limited time!
3rd row1 Month Off Available on all unit types for a limited time!
4th row1 Month Off Available on all unit types for a limited time!
5th row1 Month Off Available on all unit types for a limited time!
ValueCountFrequency (%)
free 1735
 
5.4%
on 1660
 
5.2%
month 1216
 
3.8%
months 1199
 
3.8%
1043
 
3.3%
and 1011
 
3.2%
off 886
 
2.8%
1 707
 
2.2%
of 649
 
2.0%
rent 624
 
2.0%
Other values (208) 21116
66.3%
2024-09-19T06:11:26.649509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29436
17.3%
e 15306
 
9.0%
o 12650
 
7.4%
n 11190
 
6.6%
t 10380
 
6.1%
i 8035
 
4.7%
r 7916
 
4.7%
s 7507
 
4.4%
f 6171
 
3.6%
a 5406
 
3.2%
Other values (60) 55857
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 117804
69.4%
Space Separator 29436
 
17.3%
Decimal Number 8280
 
4.9%
Uppercase Letter 7964
 
4.7%
Other Punctuation 4396
 
2.6%
Currency Symbol 931
 
0.5%
Dash Punctuation 837
 
0.5%
Math Symbol 56
 
< 0.1%
Other Symbol 54
 
< 0.1%
Close Punctuation 48
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15306
13.0%
o 12650
10.7%
n 11190
 
9.5%
t 10380
 
8.8%
i 8035
 
6.8%
r 7916
 
6.7%
s 7507
 
6.4%
f 6171
 
5.2%
a 5406
 
4.6%
l 5212
 
4.4%
Other values (14) 28031
23.8%
Uppercase Letter
ValueCountFrequency (%)
A 1079
13.5%
F 976
12.3%
O 959
12.0%
M 839
10.5%
L 516
 
6.5%
S 460
 
5.8%
C 395
 
5.0%
J 364
 
4.6%
B 360
 
4.5%
E 275
 
3.5%
Other values (11) 1741
21.9%
Decimal Number
ValueCountFrequency (%)
1 2064
24.9%
2 1405
17.0%
0 1313
15.9%
5 1229
14.8%
4 827
10.0%
8 538
 
6.5%
6 419
 
5.1%
3 264
 
3.2%
7 121
 
1.5%
9 100
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 2018
45.9%
, 941
21.4%
! 461
 
10.5%
& 425
 
9.7%
; 327
 
7.4%
/ 134
 
3.0%
: 90
 
2.0%
Currency Symbol
ValueCountFrequency (%)
$ 877
94.2%
€ 54
 
5.8%
Space Separator
ValueCountFrequency (%)
29436
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 837
100.0%
Math Symbol
ValueCountFrequency (%)
+ 56
100.0%
Other Symbol
ValueCountFrequency (%)
â„¢ 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125768
74.0%
Common 44086
 
26.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15306
12.2%
o 12650
 
10.1%
n 11190
 
8.9%
t 10380
 
8.3%
i 8035
 
6.4%
r 7916
 
6.3%
s 7507
 
6.0%
f 6171
 
4.9%
a 5406
 
4.3%
l 5212
 
4.1%
Other values (35) 35995
28.6%
Common
ValueCountFrequency (%)
29436
66.8%
1 2064
 
4.7%
. 2018
 
4.6%
2 1405
 
3.2%
0 1313
 
3.0%
5 1229
 
2.8%
, 941
 
2.1%
$ 877
 
2.0%
- 837
 
1.9%
4 827
 
1.9%
Other values (15) 3139
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169692
99.9%
Letterlike Symbols 54
 
< 0.1%
None 54
 
< 0.1%
Currency Symbols 54
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29436
17.3%
e 15306
 
9.0%
o 12650
 
7.5%
n 11190
 
6.6%
t 10380
 
6.1%
i 8035
 
4.7%
r 7916
 
4.7%
s 7507
 
4.4%
f 6171
 
3.6%
a 5406
 
3.2%
Other values (57) 55695
32.8%
Letterlike Symbols
ValueCountFrequency (%)
â„¢ 54
100.0%
None
ValueCountFrequency (%)
â 54
100.0%
Currency Symbols
ValueCountFrequency (%)
€ 54
100.0%
Distinct1699
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
Minimum2019-11-26 16:40:22.280000
Maximum2020-08-12 19:21:59.631000
2024-09-19T06:11:26.931926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:27.187970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

old_price
Real number (ℝ)

HIGH CORRELATION 

Distinct1700
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2416.5781
Minimum190
Maximum18500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:27.429987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile1355.5
Q11800.5
median2175
Q32682.5
95-th percentile4262
Maximum18500
Range18310
Interquartile range (IQR)882

Descriptive statistics

Standard deviation1047.1939
Coefficient of variation (CV)0.43333749
Kurtosis28.719138
Mean2416.5781
Median Absolute Deviation (MAD)427
Skewness3.5978363
Sum8436274
Variance1096615
MonotonicityNot monotonic
2024-09-19T06:11:27.680462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1740 13
 
0.4%
2087 12
 
0.3%
1655 11
 
0.3%
1695 10
 
0.3%
1675 10
 
0.3%
1250 9
 
0.3%
1660 9
 
0.3%
1710 9
 
0.3%
1659 8
 
0.2%
1885 8
 
0.2%
Other values (1690) 3392
97.2%
ValueCountFrequency (%)
190 1
 
< 0.1%
899 3
0.1%
951 1
 
< 0.1%
986 1
 
< 0.1%
995 3
0.1%
999 2
0.1%
1004 2
0.1%
1009 1
 
< 0.1%
1019 2
0.1%
1050 2
0.1%
ValueCountFrequency (%)
18500 1
< 0.1%
13378 1
< 0.1%
13000 1
< 0.1%
11000 1
< 0.1%
9855 1
< 0.1%
8501 1
< 0.1%
8491 1
< 0.1%
8450 1
< 0.1%
8058 1
< 0.1%
8033 1
< 0.1%

new_price
Real number (ℝ)

HIGH CORRELATION 

Distinct1697
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2386.7754
Minimum919
Maximum13750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:27.930910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum919
5-th percentile1349
Q11765
median2150
Q32655.5
95-th percentile4256.5
Maximum13750
Range12831
Interquartile range (IQR)890.5

Descriptive statistics

Standard deviation1013.806
Coefficient of variation (CV)0.42475971
Kurtosis16.575732
Mean2386.7754
Median Absolute Deviation (MAD)425
Skewness2.9609702
Sum8332233
Variance1027802.7
MonotonicityNot monotonic
2024-09-19T06:11:28.205139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2195 11
 
0.3%
1935 11
 
0.3%
1980 10
 
0.3%
1660 10
 
0.3%
1915 9
 
0.3%
1735 9
 
0.3%
2175 9
 
0.3%
2295 9
 
0.3%
1340 9
 
0.3%
1765 9
 
0.3%
Other values (1687) 3395
97.3%
ValueCountFrequency (%)
919 3
0.1%
949 1
 
< 0.1%
984 1
 
< 0.1%
995 1
 
< 0.1%
1008 1
 
< 0.1%
1018 2
0.1%
1041 1
 
< 0.1%
1046 1
 
< 0.1%
1065 3
0.1%
1084 2
0.1%
ValueCountFrequency (%)
13750 1
< 0.1%
13468 1
< 0.1%
11500 1
< 0.1%
9500 1
< 0.1%
8558 1
< 0.1%
8548 1
< 0.1%
8480 1
< 0.1%
8316 1
< 0.1%
7999 1
< 0.1%
7914 1
< 0.1%

max_price
Real number (ℝ)

HIGH CORRELATION 

Distinct324
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2898.4847
Minimum951
Maximum18500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:28.487632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum951
5-th percentile1530
Q12135
median2608
Q33100
95-th percentile4977.5
Maximum18500
Range17549
Interquartile range (IQR)965

Descriptive statistics

Standard deviation1546.1649
Coefficient of variation (CV)0.53343905
Kurtosis35.725634
Mean2898.4847
Median Absolute Deviation (MAD)483
Skewness4.8154813
Sum10118610
Variance2390625.9
MonotonicityNot monotonic
2024-09-19T06:11:28.734099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2362 185
 
5.3%
2888 142
 
4.1%
1865 80
 
2.3%
2325 72
 
2.1%
2940 69
 
2.0%
2861 63
 
1.8%
2459 59
 
1.7%
1966 59
 
1.7%
4203 45
 
1.3%
1530 45
 
1.3%
Other values (314) 2672
76.5%
ValueCountFrequency (%)
951 4
 
0.1%
1019 4
 
0.1%
1050 1
 
< 0.1%
1117 10
0.3%
1175 1
 
< 0.1%
1201 1
 
< 0.1%
1300 2
 
0.1%
1310 2
 
0.1%
1326 5
0.1%
1340 1
 
< 0.1%
ValueCountFrequency (%)
18500 10
 
0.3%
13468 1
 
< 0.1%
11000 28
0.8%
9855 3
 
0.1%
8558 10
 
0.3%
8480 1
 
< 0.1%
8316 1
 
< 0.1%
8058 16
0.5%
7791 1
 
< 0.1%
7640 3
 
0.1%

net_price
Real number (ℝ)

HIGH CORRELATION 

Distinct1712
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2287.6671
Minimum919
Maximum13468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:28.984427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum919
5-th percentile1310
Q11700.5
median2053
Q32544
95-th percentile4053.5
Maximum13468
Range12549
Interquartile range (IQR)843.5

Descriptive statistics

Standard deviation966.56567
Coefficient of variation (CV)0.4225115
Kurtosis16.840471
Mean2287.6671
Median Absolute Deviation (MAD)393
Skewness2.9680482
Sum7986246
Variance934249.19
MonotonicityNot monotonic
2024-09-19T06:11:29.266819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1615 10
 
0.3%
1992 9
 
0.3%
1685 9
 
0.3%
1849 9
 
0.3%
2175 8
 
0.2%
1735 8
 
0.2%
1660 8
 
0.2%
1742 8
 
0.2%
1755 8
 
0.2%
1935 8
 
0.2%
Other values (1702) 3406
97.6%
ValueCountFrequency (%)
919 3
0.1%
929 1
 
< 0.1%
949 1
 
< 0.1%
984 1
 
< 0.1%
1008 1
 
< 0.1%
1018 2
0.1%
1041 1
 
< 0.1%
1046 1
 
< 0.1%
1065 3
0.1%
1080 1
 
< 0.1%
ValueCountFrequency (%)
13468 1
< 0.1%
12833 1
< 0.1%
10733 1
< 0.1%
8867 1
< 0.1%
8480 1
< 0.1%
8316 1
< 0.1%
7999 1
< 0.1%
7987 1
< 0.1%
7978 1
< 0.1%
7914 1
< 0.1%

price_per_sqft
Real number (ℝ)

Distinct1586
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8781558
Minimum1.429
Maximum5.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:29.525141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.429
5-th percentile2.102
Q12.4835
median2.836
Q33.199
95-th percentile3.8215
Maximum5.861
Range4.432
Interquartile range (IQR)0.7155

Descriptive statistics

Standard deviation0.54722763
Coefficient of variation (CV)0.19013134
Kurtosis1.4007697
Mean2.8781558
Median Absolute Deviation (MAD)0.357
Skewness0.75694571
Sum10047.642
Variance0.29945808
MonotonicityNot monotonic
2024-09-19T06:11:29.767396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.934 9
 
0.3%
2.694 9
 
0.3%
3.27 9
 
0.3%
2.613 9
 
0.3%
2.728 8
 
0.2%
2.057 8
 
0.2%
2.393 8
 
0.2%
2.293 8
 
0.2%
2.403 8
 
0.2%
2.987 8
 
0.2%
Other values (1576) 3407
97.6%
ValueCountFrequency (%)
1.429 1
< 0.1%
1.501 1
< 0.1%
1.509 1
< 0.1%
1.518 1
< 0.1%
1.522 2
0.1%
1.533 1
< 0.1%
1.538 1
< 0.1%
1.562 1
< 0.1%
1.576 2
0.1%
1.588 1
< 0.1%
ValueCountFrequency (%)
5.861 1
< 0.1%
5.629 1
< 0.1%
5.217 1
< 0.1%
5.198 1
< 0.1%
5.194 1
< 0.1%
5.189 2
0.1%
5.185 1
< 0.1%
5.147 1
< 0.1%
5.062 1
< 0.1%
5.01 2
0.1%
Distinct2107
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
2024-09-19T06:11:30.422192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.7250072
Min length5

Characters and Unicode

Total characters23477
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1300 ?
Unique (%)37.2%

Sample

1st row-26.01%
2nd row-21.08%
3rd row-29.61%
4th row-30.23%
5th row-33.10%
ValueCountFrequency (%)
0.00 86
 
2.5%
6.67 20
 
0.6%
10.00 14
 
0.4%
6.65 9
 
0.3%
3.47 7
 
0.2%
6.68 7
 
0.2%
11.90 7
 
0.2%
16.09 7
 
0.2%
16.67 7
 
0.2%
7.78 6
 
0.2%
Other values (2097) 3321
95.1%
2024-09-19T06:11:31.294709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3491
14.9%
% 3491
14.9%
- 3405
14.5%
1 2122
9.0%
2 1965
8.4%
0 1428
6.1%
3 1423
6.1%
4 1090
 
4.6%
7 1061
 
4.5%
5 1053
 
4.5%
Other values (3) 2948
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13090
55.8%
Other Punctuation 6982
29.7%
Dash Punctuation 3405
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2122
16.2%
2 1965
15.0%
0 1428
10.9%
3 1423
10.9%
4 1090
8.3%
7 1061
8.1%
5 1053
8.0%
6 1041
8.0%
9 964
7.4%
8 943
7.2%
Other Punctuation
ValueCountFrequency (%)
. 3491
50.0%
% 3491
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 3405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23477
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3491
14.9%
% 3491
14.9%
- 3405
14.5%
1 2122
9.0%
2 1965
8.4%
0 1428
6.1%
3 1423
6.1%
4 1090
 
4.6%
7 1061
 
4.5%
5 1053
 
4.5%
Other values (3) 2948
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3491
14.9%
% 3491
14.9%
- 3405
14.5%
1 2122
9.0%
2 1965
8.4%
0 1428
6.1%
3 1423
6.1%
4 1090
 
4.6%
7 1061
 
4.5%
5 1053
 
4.5%
Other values (3) 2948
12.6%

cooperate
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
True
3491 
ValueCountFrequency (%)
True 3491
100.0%
2024-09-19T06:11:31.523746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
False
1851 
True
1640 
ValueCountFrequency (%)
False 1851
53.0%
True 1640
47.0%
2024-09-19T06:11:31.668915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-09-19T06:11:18.440347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:07.080877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:08.726957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:10.657960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:12.152519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:13.887152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:15.449520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:16.944224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:18.625088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:07.397075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:08.944802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:10.834724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:12.450553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:14.080513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:15.635211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:17.137241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:18.811205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:07.582499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:09.453169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:11.037062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:12.640417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:14.274044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:15.828518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:17.322542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:18.988795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:07.752018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:09.623950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:11.204575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:12.889857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:14.451087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:16.005373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:17.507799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:19.181841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:07.944935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:09.825062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:11.402857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:13.075326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:14.652424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:16.203595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:17.700619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:19.358751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:08.137645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:10.053225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:11.583145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:13.284815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:14.837524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:16.396745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:17.885808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:19.543653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:08.340195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:10.288374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:11.786943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:13.518106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:15.038489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:16.573940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:18.071122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:19.719990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:08.525332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:10.473761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:11.983986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:13.708471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:15.264676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:16.767187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-19T06:11:18.263670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-19T06:11:31.808285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
bathsbedsbuilding_idconcession_appliedmax_priceneighborhood_namenet_pricenew_priceold_priceprice_per_sqftsqft
baths1.0000.566-0.0370.0530.5970.1890.5670.5650.564-0.1100.656
beds0.5661.0000.1150.1190.5850.3740.5340.5550.5660.2960.641
building_id-0.0370.1151.0000.055-0.0080.455-0.060-0.050-0.0180.106-0.137
concession_applied0.0530.1190.0551.0000.1980.4220.0510.1180.0980.1700.054
max_price0.5970.585-0.0080.1981.0000.2120.8760.8890.8870.2890.713
neighborhood_name0.1890.3740.4550.4220.2121.0000.1330.1320.1370.2380.286
net_price0.5670.534-0.0600.0510.8760.1331.0000.9830.9740.3380.797
new_price0.5650.555-0.0500.1180.8890.1320.9831.0000.9890.3180.795
old_price0.5640.566-0.0180.0980.8870.1370.9740.9891.0000.3090.792
price_per_sqft-0.1100.2960.1060.1700.2890.2380.3380.3180.3091.000-0.223
sqft0.6560.641-0.1370.0540.7130.2860.7970.7950.792-0.2231.000

Missing values

2024-09-19T06:11:20.090410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-19T06:11:20.746590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-19T06:11:21.471408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

building_idnameunit_numberbedsbathssqftneighborhood_nameconcession_titleupdated_onold_pricenew_pricemax_pricenet_priceprice_per_sqftpercentage_changecooperateconcession_applied
011000 South Clark250711.0730South Loop1 Month Off Available on all unit types for a limited time!2020-07-23 16:47:51.66122682313291821592.957-26.01%TrueTrue
111000 South Clark161511.0765South Loop1 Month Off Available on all unit types for a limited time!2020-08-01 19:46:25.88324192467291823033.010-21.08%TrueTrue
211000 South Clark180611.0645South Loop1 Month Off Available on all unit types for a limited time!2020-08-05 11:30:16.88621582201291820543.185-29.61%TrueTrue
311000 South Clark271711.0710South Loop1 Month Off Available on all unit types for a limited time!2020-08-01 11:33:52.35321782181291820362.867-30.23%TrueTrue
411000 South Clark181711.0710South Loop1 Month Off Available on all unit types for a limited time!2020-08-01 11:33:52.35320882091291819522.749-33.10%TrueTrue
511000 South Clark141711.0710South Loop1 Month Off Available on all unit types for a limited time!2020-08-01 11:33:52.35320482051291819142.696-34.41%TrueTrue
611000 South Clark100211.0660South Loop1 Month Off Available on all unit types for a limited time!2020-08-11 05:49:52.67020032006291818722.837-35.85%TrueTrue
711000 South Clark260911.0722South Loop1 Month Off Available on all unit types for a limited time!2020-08-01 11:33:52.35323382342291821863.028-25.09%TrueTrue
811000 South Clark71511.0765South Loop1 Month Off Available on all unit types for a limited time!2020-08-12 11:38:20.53523282283291821312.785-26.97%TrueTrue
911000 South Clark60911.0722South Loop1 Month Off Available on all unit types for a limited time!2020-08-01 11:33:52.35323282332291821773.015-25.39%TrueTrue
building_idnameunit_numberbedsbathssqftneighborhood_nameconcession_titleupdated_onold_pricenew_pricemax_pricenet_priceprice_per_sqftpercentage_changecooperateconcession_applied
34816378The Bernardin240122.01399River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:12.76538303814383034332.454-10.37%TrueTrue
34826378The Bernardin230922.01255River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:21.47435633548383031932.544-16.63%TrueTrue
34836378The Bernardin70122.01405River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:18.73432783265383029392.091-23.26%TrueTrue
34846378The Bernardin210922.01261River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:12.76529832971383026742.120-30.18%TrueTrue
34856378The Bernardin230122.01399River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:19.75335223508383031572.257-17.57%TrueTrue
34866378The Bernardin80122.01405River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:13.83932893275383029482.098-23.03%TrueTrue
34876378The Bernardin220122.01399River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:09.41136223606383032452.320-15.27%TrueTrue
34886378The Bernardin220422.01244River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:13.48833753361383030252.432-21.02%TrueTrue
34896378The Bernardin230622.01174River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:21.47434093396383030562.603-20.21%TrueTrue
34906378The Bernardin100122.01405River NorthCurrently offering 1.5 months free if move in is by August 25th. 1 month free for on everything else.2020-08-12 10:17:18.73433093296383029662.111-22.56%TrueTrue

Duplicate rows

Most frequently occurring

building_idnamebedsbathssqftneighborhood_nameconcession_titleupdated_onold_pricenew_pricemax_pricenet_priceprice_per_sqftpercentage_changecooperateconcession_applied# duplicates
17100Park Michigan22.0968South LoopNaN2020-08-02 11:24:19.94122972298277419922.057-28.19%TrueTrue5
19106Ravenswood Terrace11.0632RavenswoodNaN2020-08-07 15:01:55.46317401618186516182.560-13.24%TrueFalse5
010215 West Apartments00.0517The Loop.2020-08-06 07:42:36.63316101620180016203.133-10.00%TrueFalse3
110215 West Apartments00.0656The Loop.2020-08-06 07:42:36.63316951660180016602.530-7.78%TrueFalse3
310215 West Apartments00.0656The Loop.2020-08-06 07:42:36.63317251690180016902.576-6.11%TrueFalse3
410215 West Apartments00.0656The Loop.2020-08-06 07:42:36.63317351700180017002.591-5.56%TrueFalse3
16100Park Michigan22.0968South LoopNaN2020-08-02 11:24:19.94122472248277419482.013-29.78%TrueTrue3
20106Ravenswood Terrace11.0632RavenswoodNaN2020-08-07 15:01:55.46317651643186516432.600-11.90%TrueFalse3
210215 West Apartments00.0656The Loop.2020-08-06 07:42:36.63317151680180016802.561-6.67%TrueFalse2
510215 West Apartments11.0750The Loop.2020-08-06 07:42:36.63321552115225521152.820-6.21%TrueFalse2